Explainable AI: Advances in Interpretability Algorithms and Applications
A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Evolutionary Algorithms and Machine Learning".
Deadline for manuscript submissions: 28 February 2026 | Viewed by 878
Special Issue Editor
Interests: machine learning; artificial intelligence; e-learning; programming languages
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The growing integration of artificial intelligence (AI) in critical domains such as healthcare, finance, law, and autonomous systems has intensified the need for models that are not only accurate, but also interpretable and transparent. In response, explainable artificial intelligence (XAI) has emerged as a field dedicated to opening the “black box” of complex AI systems, enabling human users to understand, trust, and effectively govern intelligent algorithms.
This Special Issue will focus on recent advances in explainability algorithms—both model-specific and model-agnostic—that aim to improve the transparency, accountability, and robustness of AI systems across diverse applications. As AI continues to be deployed in high-stakes environments, the development of reliable interpretability tools becomes essential for ensuring ethical use, regulatory compliance, and human-centric design.
We invite high-quality submissions that address the theoretical foundations, algorithmic innovations, and real-world implementations of explainable AI. We particularly welcome interdisciplinary research that bridges technical development with societal, legal, or ethical considerations.
Topics of interest include, but are not limited to, the following:
- Novel algorithms for local and global interpretability;
- Comparative studies of XAI methods (e.g., SHAP, LIME, Integrated Gradients, Anchors);
- Benchmarks, metrics, and evaluation frameworks for XAI;
- Explainability in ensemble learning, deep learning, and generative models;
- Interpretable AI in healthcare, finance, law, and scientific discovery;
- Human-in-the-loop and interactive explanations;
- Visualization techniques for explainable AI;
- The role of explainability in AI ethics, fairness, and accountability;
- Regulatory perspectives and standards for transparent AI;
- Robustness and reliability of explanation methods under adversarial conditions;
- Causal inference and counterfactual reasoning in XAI;
- Usability and cognitive dimensions of model explanations;
- Explainability in edge computing, IoT, and real-time systems.
We welcome original research articles, surveys, case studies, and critical reviews that contribute to advancing the field of interpretable and explainable artificial intelligence.
Dr. Antonio Sarasa-Cabezuelo
Guest Editor
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- explainable AI (XAI)
- interpretability algorithms
- SHAP, LIME, Anchors
- transparent machine learning
- trustworthy AI
- human-centered AI
- algorithmic accountability
- model-agnostic explainability
- fairness and bias in AI
- XAI for high-stakes applications
- AI ethics and governance
- interpretable deep learning
- AI transparency frameworks
- responsible AI design
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